A sample selection strategy for next-generation sequencing.
A sample selection strategy for next-generation sequencing.
复制标题
DOI：
10.1002/gepi.21664
复制
发表时间：
2012-11
影响因子：
2.1
通讯作者：
Marjoram, Paul
中科院分区：
文献类型：
--
作者：
Kang, Chul Joo;Marjoram, Paul
下一代测序技术为我们提供了大量的序列数据。它比以前的测序技术更有效，更便宜，但对整个样品进行深度重测序仍然很昂贵。因此，需要选择样本子集进行测序的合理策略。在这里，我们描述了一种算法，用于选择现有样本的子样本，如果一个人有两个可能的目标：最大限度地提高检测到的新多态位点的数量，或提高效率，剩余的未测序的个人可以有他们的类型在新发现的多态性。然后，我们描述了我们的算法的变化，更侧重于检测罕见的变种。我们证明了我们的算法使用模拟数据和数据从1000个基因组计划的性能。
Next-generation sequencing technology provides us with vast amounts of sequence data. It is efficient, and cheaper than previous sequencing technologies, but deep resequencing of entire samples is still expensive. Therefore, sensible strategies for choosing subsets of samples to sequence are required. Here we describe an algorithm for selection of a sub-sample of an existing sample if one has either of two possible goals in mind: maximizing the number of new polymorphic sites that are detected, or improving the efficiency with which the remaining unsequenced individuals can have their types imputed at newly discovered polymorphisms. We then describe a variation on our algorithm that is more focused on detecting rarer variants. We demonstrate the performance of our algorithm using simulated data and data from the 1000 Genomes Project.
登录
查看更多内容
影响因子：
4.5
作者：
Liu DJ;Leal SM
通讯作者：
Leal SM
影响因子：
9.8
作者：
Li, Bingshan;Leal, Suzanne M.
通讯作者：
Leal, Suzanne M.
影响因子：
1.5
作者：
Slatkin, Montgomery
通讯作者：
Slatkin, Montgomery
影响因子：
2.1
作者：
Li, Yun;Willer, Cristen J.;Ding, Jun;Scheet, Paul;Abecasis, Goncalo R.
通讯作者：
Abecasis, Goncalo R.
影响因子：
4.5
作者：
Neale BM;Rivas MA;Voight BF;Altshuler D;Devlin B;Orho-Melander M;Kathiresan S;Purcell SM;Roeder K;Daly MJ
通讯作者：
Daly MJ